<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper uses a clustering algorithm to extract and classify credit rating features of tobacco retailers and evaluates whether the classification results are reasonable by combining clustering evaluation indexes. The distance between samples is calculated using the similarity measure. The natural domain method density and peak clustering method are used to analyze the distribution of sample points in the data set. Combining the cluster analysis creates the tobacco retail credit rating evaluation index. The results show that cluster analysis can effectively extract credit rating features from tobacco retailers. When the number of features is 25, the model has the best classification effect, with a classification accuracy rate of 91.1%, a recall rate of 91.5%, and an F1 value of 91.3%. The classification of tobacco retailers’ credit ratings can be improved effectively by the research in this paper.</p>
</abstract>

This paper uses a clustering algorithm to extract and classify credit rating features of tobacco retailers and evaluates whether the classification results are reasonable by combining clustering evaluation indexes. The distance between samples is calculated using the similarity measure. The natural domain method density and peak clustering method are used to analyze the distribution of sample points in the data set. Combining the cluster analysis creates the tobacco retail credit rating evaluation index. The results show that cluster analysis can effectively extract credit rating features from tobacco retailers. When the number of features is 25, the model has the best classification effect, with a classification accuracy rate of 91.1%, a recall rate of 91.5%, and an F1 value of 91.3%. The classification of tobacco retailers’ credit ratings can be improved effectively by the research in this paper.

Application of unsupervised machine learning algorithms to credit classification methods for tobacco retailers

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper uses a clustering algorithm to extract and classify credit rating features of tobacco retailers and evaluates whether the classification results are...

Application of unsupervised machine learning algorithms to credit classification methods for tobacco retailers

Pubblicato online: 01 nov 2023

Pagine: -

Ricevuto: 24 dic 2022

Accettato: 12 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.00940

Parole chiave
Clustering algorithm, Similarity measure, Natural domain approach, Credit rating, Tobacco retailers

© 2023 Lili Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of unsupervised machine learning algorithms to credit classification methods for tobacco retailers

Pubblicato online: 01 nov 2023

Pagine: -

Ricevuto: 24 dic 2022

Accettato: 12 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.00940

Parole chiaveClustering algorithm, Similarity measure, Natural domain approach, Credit rating, Tobacco retailers

© 2023 Lili Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Clustering algorithm, Similarity measure, Natural domain approach, Credit rating, Tobacco retailers